Portfolio Performance Analysis Report
Judy Northrop
2025-06-22
Executive Summary
This report analyzes the performance of our optimized portfolio strategy across different market regimes and compares it against relevant benchmarks.
Portfolio Optimization Methodology
Our portfolio optimization strategy leverages advanced quantitative methods to construct liquid S&P 500 sector-diverse portfolios using principles of Markowitz Modern Portfolio Theory (MPT). The optimization framework combines cutting-edge statistical techniques with robust numerical optimization to deliver superior risk-adjusted returns.
Key Components:
1. Covariance Estimation with Ledoit-Wolf Shrinkage - We employ the Ledoit-Wolf shrinkage estimator to obtain more reliable covariance matrix estimates - This approach addresses the notorious instability of sample covariance matrices, particularly in high-dimensional settings - The shrinkage technique combines the sample covariance with a structured target matrix, reducing estimation error and improving out-of-sample performance
2. Sharpe Ratio Maximization with DEoptim - Portfolio weights are optimized using Differential Evolution (DEoptim), a robust global optimization algorithm - Our objective function maximizes the Sharpe ratio using a 4% annual risk-free rate assumption - This approach optimizes the risk-adjusted return by maximizing excess return per unit of volatility
3. Sector Diversification and Liquidity Constraints - Stock selection focuses on liquid S&P 500 constituents across all major sectors - Position sizing constraints (2-10% per holding) ensure proper diversification and risk management - Regular rebalancing (30-day frequency) maintains optimal allocation while controlling transaction costs
This methodology produces portfolios that are theoretically grounded in modern portfolio theory while being practically implementable with real-world constraints and market frictions.
Overall Performance
Overall Strategy Performance
Performance by Market Regime
Bull Market Period (2018-2019)
COVID Period (2020-2021)
Recovery Period (2022-2025)
Detailed Performance Metrics
| Metric | Optimized 30-day | Equal Weight | SPY | |
|---|---|---|---|---|
| Annualized Return | Annualized Return | 0.2002 | 0.0631 | 0.0466 |
| Annualized Std Dev | Annualized Std Dev | 0.2356 | 0.2841 | 0.2549 |
| Annualized Sharpe (Rf=0%) | Annualized Sharpe (Rf=0%) | 0.8497 | 0.2220 | 0.1829 |
| Semi Deviation | Semi Deviation | 0.0105 | 0.0126 | 0.0114 |
| Gain Deviation | Gain Deviation | 0.0091 | 0.0110 | 0.0096 |
| Loss Deviation | Loss Deviation | 0.0088 | 0.0108 | 0.0098 |
| Downside Deviation (MAR=210%) | Downside Deviation (MAR=210%) | 0.0152 | 0.0175 | 0.0164 |
| Downside Deviation (Rf=0%) | Downside Deviation (Rf=0%) | 0.0100 | 0.0124 | 0.0112 |
| Downside Deviation (0%) | Downside Deviation (0%) | 0.0100 | 0.0124 | 0.0112 |
| Maximum Drawdown | Maximum Drawdown | 0.4058 | 0.4072 | 0.4339 |
| Historical VaR (95%) | Historical VaR (95%) | -0.0241 | -0.0288 | -0.0259 |
| Historical ES (95%) | Historical ES (95%) | -0.0298 | -0.0368 | -0.0328 |
| Modified VaR (95%) | Modified VaR (95%) | -0.0236 | -0.0289 | -0.0263 |
| Modified ES (95%) | Modified ES (95%) | -0.0296 | -0.0366 | -0.0333 |
| Worst Drawdown | Worst Drawdown | 0.4058 | 0.4072 | 0.4339 |
Current Portfolio Initialization
| Ticker | Company Name | Sector | Weight (%) |
|---|---|---|---|
| NVDA | NVIDIA Corporation | Technology | 10.0 |
| AAPL | Apple Inc. | Technology | 10.0 |
| MSFT | Microsoft Corporation | Technology | 9.5 |
| GOOGL | Alphabet Inc. | Technology | 8.5 |
| JPM | JPMorgan Chase & Co. | Financials | 8.0 |
| XOM | Exxon Mobil Corporation | Energy | 7.5 |
| UNH | UnitedHealth Group | Health Care | 7.0 |
| NEE | NextEra Energy | Utilities | 6.5 |
| AMT | American Tower Corp | Real Estate | 6.0 |
| PLD | Prologis Inc | Real Estate | 5.5 |
| CAT | Caterpillar Inc. | Industrials | 5.0 |
| JNJ | Johnson & Johnson | Health Care | 4.5 |
| WMT | Walmart Inc. | Consumer Discretionary | 4.0 |
| HD | Home Depot Inc. | Consumer Discretionary | 3.5 |
| PG | Procter & Gamble Co. | Consumer Discretionary | 3.0 |
| KO | Coca-Cola Company | Consumer Discretionary | 2.5 |
| TSLA | Tesla Inc. | Consumer Discretionary | 2.0 |
| VZ | Verizon Communications | Utilities | 2.0 |
Conclusions
Key Findings:
- The optimized 30-day rebalancing strategy shows consistent outperformance
- Risk-adjusted returns (Sharpe ratio) remain superior across different market regimes
- Maximum drawdown is well-controlled compared to benchmarks
Implementation:
- Initialize portfolio using the weights shown in the Current Portfolio section above
- Rebalance every 30 days to maintain optimal allocation
- Monitor regime changes for potential strategy adjustments
- Consider transaction costs when implementing
Implementation Notes:
- The current portfolio implementation uses the most recent 200 days of data
- Stocks are selected from major sectors to ensure diversification
- Minimum weight of 2% and maximum of 10% per position for risk management
- Expected metrics are based on historical optimization and may vary
To generate a live portfolio recommendation with current market data:
Rscript scripts/examples/current_portfolio_recommendation.RReport generated on 2025-06-22 using ggplot2 performance visualization
Disclaimer: This report is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always consult a licensed financial advisor before making investment decisions.